Gradient vector flow: Difference between revisions

Revision as of 04:38, 30 March 2020

Revision as of 04:59, 30 March 2020

Line 2:

'''Gradient vector flow''' ('''GVF'''), a [[computer vision]] framework introduced by [http://iacl.ece.jhu.edu/~chenyang/ Chenyang Xu] and [http://www.iacl.ece.jhu.edu/index.php/Prince Jerry L. Prince]

<ref name=":1">{{ Cite conference | last1 = Xu | first1 = C. Xu| last2 = Prince | first2 and= J.L. Prince,| title = "Gradient Vector Flow: A New External Force for Snakes," | book-title = Proc. IEEE Conf. on Comp. Vis. Patt. Recog. (CVPR), | place = Los Alamitos: | publisher = Comp. Soc. Press, pp.| pages = 66–71, | date = June 1997, | url = http://iacl.ece.jhu.edu/pubs/p087c.pdf}}</ref>

<ref name=":2">{{Cite journal | title = Snakes, Shapes, and Gradient Vector Flow| journal = IEEE Transactions on Image Processing | volume = 7| issue = 3| pages = 359-369| year = 1998| last1 = Xu | first1 = C.| last2 = Prince | first2 = J. L. | url = http://iacl.ece.jhu.edu/pubs/p084j.pdf}}</ref>, is the vector field that is produced by a process that smooths and diffuses an input vector field, and is usually used to create a vector field that points to object edges from a distance. It's widely used in object tracking, shape recognition, [[Image segmentation|segmentation]], and [[edge detection]]. In particular, it's commonly used in conjunction with [[active contour model]].

Line 20:

Although GVF was designed originally for the purpose of segmenting objects using active contours attracted to edges, it has been since

adapted and used for many alternative purposes. Some newer purposes including defining a continuous medial axis representation<ref name=":3">{{Cite journal | title = Variational curve skeletons using gradient vector flow | journal = IEEE Transactions on Pattern Analysis and Machine Intelligence | volume = 31| issue = 12| pages = 2257–2274| year = 2009| last1 = Hassouna | first1 = M.S.| last2 = Farag | first2 = A.Y. }}</ref>, regularizing image anisotropic diffusion algorithms<ref name=":YuxTIP06">{{Cite journal | title = GVF-based anisotropic diffusion models | journal = IEEE Transactions on Image Processing | volume = 15 | issue = 6 | pages = 1517--1524 | year = 2006 | last1 = Yu | first1 = H. | last2 = Chua | first2 = C.S. }}</ref>, finding the centers of ribbon-like objects<ref name=":HanxNI04">{{Cite journal | title = CRUISE: cortical reconstruction using implicit surface evolution | journal = NeuroImage | volume = 23 | number = 3 | pages = 997--1012 | year = 2004 | last1 = Han | first1 = X. | last2 = Pham | first2 = D.L. | last3 = Tosun | first3 = D. | last4 = Rettmann | first4 = M.E. | last5 = Xu | first5 = C. | last6 = Prince | first6 = J.L. | display-authors = etal }}</ref>, constructing graphs for optimal surface segmentations<ref name=":MirxCMIG17"> {{Cite journal | title = Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes | journal = Computerized Medical Imaging and Graphics | volume = 55 | pages = 87-94 | year = 2017 | last1 = Miri | first1 = M.S., | last2 = Robles | first2 = V.A. | last3 = Abràmoff | first3 = M.D. | last4 = Kwon | first4 = Y.H. | last5 = Garvin | first5 = M.K.}}</ref>, creating a shape prior~\cite{<ref name=":BaixCMIG18"> {{Cite journal | title = Optimal multi-object segmentation with novel gradient vector flow based shape priors | journal = Computerized Medical Imaging and Graphics | volume=69 | pages= 96-111 | year= 2018 | publisher=Elsevier | last1 = Bai | first1 = J. | last2 = Shah | first2 = A. | last3 = Wu | first3 = X.}}</ref>, and much more.

==Related Concepts==